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Tightly Coupled GPS/INS/CCD Integration Based on GPS Carrier Phase Velocity Update

机译:基于GPS载波相位速度更新的紧密耦合GPS / INS / CCD集成

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In the majority of multi-sensor mapping systems, the GPS/INS integration serves as a navigation and georegistration tool, supporting the direct orientation of the simultaneously acquired imagery. In a tightly coupled integration architecture, where double difference (DD) GPS carrier phase observations and the IMU (inertial measurement unit) accelerometer and gyroscope data are integrated in a single centralized Kalman filter, any loss of GPS signal causes a discontinuity in the filter measurement update until the signal reacquisition and the integer ambiguity recovery. Long GPS gaps may cause IMU error growth, and thus, positioning error increase, which may prevent instantaneous ambiguity recovery, especially for longer baselines and noisy environments. If the time needed to fix the new ambiguities is excessive, preventing the filter from using any GPS information, it may be beneficial to switch to either triple differences (they do not require ambiguity fixing, as ambiguities are eliminated by the third difference), or to velocities derived from the carrier phase observations. Extracting the GPS velocity from the carrier phase observation, which is independent of the ambiguity value, offers an opportunity to calibrate the IMU sensors, and to maintain the high quality navigation parameter estimation. This paper presents a filter update method based on GPS velocity calibration in the GPS/INS integration system, to assure availability and continuity of the navigation solution. The algorithm has been implemented and validated based on the data obtained by the AIMS system, originally developed at the Center for Mapping, The Ohio State University. AIMS utilizes the DD GPS carrier phase observable as the measurement update (and requires integer ambiguity values), while the method presented here is based on introducing the GPS velocity derived from the carrier phase observable to bridge these portions of the trajectory, where GPS signal is partially or entirely available but the integer ambiguities cannot be established. The preliminary results indicate that a steady navigation accuracy can be maintained for the entire trajectory. Moreover, the GPS velocity information, combined with the DD carrier phase, is likely to improve the filter's sensitivity to the platform maneuvers.
机译:在大多数多传感器映射系统中,GPS / INS集成用作导航和地理位置工具,支持同时获取的图像的直接方向。在紧密耦合的集成架构中,其中双差(DD)GPS载波相位观察和IMU(惯性测量单元)加速度计和陀螺数据集成在单个集中的卡尔曼滤波器中,任何GPS信号的丢失都会导致滤波器测量中的不连续性更新直到信号重新命中和整数歧义恢复。 LONG GPS间隙可能导致IMU错误的增长,因此定位误差增加,这可能会阻止瞬时模糊性恢复,特别是对于更长的基线和嘈杂的环境。如果修复新的歧义所需的时间过多,则防止过滤器使用任何GPS信息,切换到三重差异可能是有益的(它们不需要歧义固定,因为第三差异消除了歧义,或者从载体相位观察结果衍生的速度。从载波相位观察中提取GPS速度,它们与歧义值无关,提供了校准IMU传感器的机会,并保持高质量的导航参数估计。本文介绍了基于GPS / INS集成系统中GPS速度校准的过滤器更新方法,以确保导航解决方案的可用性和连续性。该算法基于由AIMS系统获得的数据实现和验证,最初在俄亥俄州州立大学的映射中心开发。 AIMS利用DD GPS载波相位可观察到的测量更新(并且需要整数模糊值),而这里呈现的方法是基于引入从载波相位导出的GPS速度,以桥接GPS信号的轨迹的这些部分。部分或完全可用,但无法建立整数含糊不清。初步结果表明,可以为整个轨迹保持稳定的导航精度。此外,与DD载波相结合的GPS速度信息可能会改善滤波器对平台操纵的敏感性。

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